Penguin

A few considerations about this doc

This Quarto document serves as a practical illustration of the concepts covered in the productive workflow online course

1 Introduction

This document offers a straightforward analysis of the well-known penguin dataset. It is designed to complement the Productive R Workflow online course.

You can read more about the penguin dataset here.

Let’s load libraries before we start!

library(tidyverse)
library(hrbrthemes)    # ipsum theme for ggplot2 charts
library(patchwork)     # combine charts together

2 Loading data

The dataset has already been loaded and cleaned in the previous step of this pipeline.

Let’s load the clean version, together with a few functions available in functions.R.

# Source functions
source(file = "functions.R")

# Read the clean dataset
data <- readRDS(file = "../data/data_clean.rds")

Appraising penguins’ bill length and depth

3 Bill Length and Bill Depth

Now, let’s make some descriptive analysis, including summary statistics and graphs.

What’s striking is the slightly negative relationship between bill length and bill depth:

\[{\displaystyle Avg={\frac {1}{n}}\sum _{i=1}^{n}a_{i}={\frac {a_{1}+a_{2}+\cdots +a_{n}}{n}}}\]

library(hrbrthemes)

palmerpenguins::penguins |> 
  filter(!is.na(sex)) |> 
  ggplot(
    aes(x = bill_length_mm, y = bill_depth_mm)
  ) +
    geom_point(color = "#69b3a2") +
    labs(
      x = "Bill Length (mm)",
      y = "Bill Depth (mm)",
      title = paste("Surprising relationship?")
    ) + 
  theme_ipsum()

Relationship between bill length and bill depth. All data points included.

It is also interesting to note that bill length a and bill depth are quite different from one specie to another. This is summarized in the 2 tables below:

data %>%
 group_by(species) %>% 
  summarise(average_bill_length = mean(bill_length_mm, na.rm = TRUE))
data %>%
 group_by(species) %>% 
  summarise(average_bill_depth = mean(bill_depth_mm, na.rm = TRUE))

# A tibble: 3 × 2
  species   average_bill_length
  <chr>                   <dbl>
1 Adelie                   38.8
2 Chinstrap                48.8
3 Gentoo                   47.5

# A tibble: 3 × 2
  species   average_bill_depth
  <chr>                  <dbl>
1 Adelie                  18.3
2 Chinstrap               18.4
3 Gentoo                  15.0

The three plots are not displayed correctly unlike the author’s rendition

Now, let’s check the relationship between bill depth and bill length for the specie Adelie on the island Torgersen:

# Use the function in functions.R
p1 <- create_scatterplot(data, "Adelie", "Torgersen")
p2 <- create_scatterplot(data, "Chinstrap", "Biscoe")
p3 <- create_scatterplot(data, "Gentoo", "Dream")

(p1 + p2) / p3

4 Displaying penguins data as a DT table

library(palmerpenguins)
library(tidyverse)
library(DT)

data_clean <- readRDS("~/Documents/R-books/productive-r-workflow/data/data_clean.rds")

datatable(data_clean, filter = "top")

Making scatterplot interactive using the library ggplotly.

library(tidyverse)
library(plotly)


library(hrbrthemes)

penguins <- palmerpenguins::penguins |> 
  filter(!is.na(sex)) |> 
  ggplot(
    aes(x = bill_length_mm, y = bill_depth_mm)
  ) +
    geom_point(color = "#69b3a2") +
    labs(
      x = "Bill Length (mm)",
      y = "Bill Depth (mm)",
      title = paste("Surprising relationship?")
    ) + 
  theme_ipsum()

ggplotly(penguins)